Online multi-label streaming feature selection by affinity significance, affinity relevance and affinity redundancy

Abstract

Multi-label streaming feature selection has applied to various fields to deal with the applications that features arrive dynamically. However, most exist multi-label streaming feature selection methods ignore that a feature tends to provide more classification information for part of labels, rather than equal information for all labels. This phenomenon results part of labels get more information from selected features, while other labels lack information. In order to address the issue, we propose a novel multi-label streaming feature selection method. Firstly, we come up with the concept of affinity between features and labels. Secondly, we propose the concepts of affinity significance, affinity relevance and affinity redundancy to evaluate streaming features in three dimensions. Thirdly, we propose a novel multi-label streaming feature selection method named OMFS-FA. OMFS-FA has three phases to retain affinity significant features, remove affinity irrelevant features and remove affinity redundant features respectively. Finally, experiments on performance analysis, statistic analysis, number of selected features and running time analysis are conducted, verifying that OMFS-FA significantly outperforms other eleven methods in terms of effectiveness and efficiency.